On maneuvering target tracking via the PMHT

TitleOn maneuvering target tracking via the PMHT
Publication TypeConference Paper
Year of Publication1997
AuthorsLogothetis, A., V. Krishnamurthy, and J. Holst
Conference NameDecision and Control, 1997., Proceedings of the 36th IEEE Conference on
Pagination5024 -5029 vol.5
Date Publisheddec.
Keywordsclutter, expectation maximization algorithm, hidden Markov model, hidden Markov models, iterative method, iterative methods, jump Markov linear system, Kalman filter, Kalman filters, linear systems, maneuvering target tracking, probabilistic multiple hypothesis tracking, state estimation, state sequence, stochastic systems, target tracking

This paper presents an iterative off-line optimal state estimation algorithm, which yields the maximum a posteriori (MAP) state trajectory estimate of the state sequence of a target maneuvering in clutter. The problem is formulated as a jump Markov linear system and the expectation maximization algorithm is used to compute the state sequence estimate. The proposed algorithm optimally combines a hidden Markov model and a Kalman smoother to yield the MAP target state sequence estimate. The algorithm proposed uses probabilistic multi-hypothesis tracking (PMHT) techniques for tracking a single maneuvering target in clutter. Previous applications of the PMHT technique have addressed the problem of tracking multiple non-maneuvering targets. These techniques are extended to address the problem of optimal (in a MAP sense) tracking of a maneuvering target in clutter


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